Output GEOSPACE-1D

-Author: Concetta D'Amato and Riccardo Rigon
-License: this work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

---

%reload_ext autoreload
%autoreload 2
%matplotlib inline
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.colors as cl 
from matplotlib import rc
import matplotlib.style as style 
import math
import xarray as xr
import warnings
import plotly.graph_objects as go
import plotly.express as px
from geoframepy.timeseries import io_csv
from plotly.subplots import make_subplots


import pandas as pd
warnings.filterwarnings('ignore')
style.available
style.use('seaborn-whitegrid')
from GEOSPACE1D_Output import*

####### Plot settings #######
nice_fonts = {"legend.frameon": True, "legend.fancybox": True, "legend.facecolor": "white", "axes.edgecolor": "0.8","axes.linewidth": 0.6,"grid.linewidth":0.3,
    # Use LaTeX to write all text
    "text.usetex": False,"font.family": "Arial",
    # Use 10pt font in plots, to match 10pt font in document
    "axes.labelsize": 14,"font.size": 14,
    # Make the legend/label fonts a little smaller
    "legend.fontsize": 10,"xtick.labelsize": 14,"ytick.labelsize": 14,}
plt.rcParams.update(nice_fonts)
my_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#ece7f2","#d0d1e6","#a6bddb","#74a9cf","#3690c0","#0570b0","#045a8d","#023858"])
colors = ['#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99', '#e31a1c', '#fdbf6f', '#ff7fà00']

project_path = os.path.dirname(os.getcwd())
#project_path= '/Users/concettadamato/Eclipse_workspace_12/GEOSPACE-1D_simulation'

os.chdir('C:/Users/marco/OMS_Project/OMS_Project_GEOSPACE-1D-main/output/Montecurto')

lab = 'Montecurto_ProsperoPM_totalstress_net_07'

file_name = 'OMS_'+lab+'_0000.nc'

ds = xr.open_dataset(file_name,engine='scipy')
ds

<xarray.Dataset>
Dimensions:                 (depth: 101, dualDepth: 101, time: 8741)
Coordinates:
  * depth                   (depth) float64 -0.995 -0.985 -0.975 ... -0.005 0.0
  * dualDepth               (dualDepth) float64 -1.0 -0.99 -0.98 ... -0.01 0.0
  * time                    (time) datetime64[ns] 2023-04-14T19:00:00 ... 202...
Data variables: (12/20)
    psi                     (time, depth) float64 ...
    psiIC                   (depth) float64 -0.005 -0.015 -0.025 ... -0.995 -1.0
    rootDensityIC           (depth) float64 0.0 0.0 0.0 ... 2.15e-05 0.0
    Temperature             (depth) float64 273.1 273.1 273.1 ... 273.1 293.1
    theta                   (time, depth) float64 ...
    waterVolume             (time, depth) float64 ...
    ...                      ...
    StressedTs              (time, depth) float64 ...
    StressedEs              (time, depth) float64 ...
    RootDensity             (time, depth) float64 ...
    errorWaterVolume        (time) float64 1.277e-12 -1.445e-17 ... 1.326e-17
    runOff                  (time) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
    controlVolume           (depth) float64 0.01 0.01 0.01 ... 0.01 0.01 1.0
Attributes:
    Description_of_the_problem:         GEOSPACE simulation
    Top_boundary_condition:             Top Coupled
    Bottom_boundary_condition:          Bottom Free Drainage
    path_top_boundary_condition:        C:\Users\marco\OMS_Project\OMS_Projec...
    path_bottom_boundary_condition:     C:\Users\marco\OMS_Project\OMS_Projec...
    path_grid:                          C:\Users\marco\OMS_Project\OMS_Projec...
    time_delta:                          
    swrc_model:                         {Water Depth,Van Genuchten}
    soil_hydraulic_conductivity_model:  {null,Mualem Van Genuchten}
    interface_conductivity_model:       max

xarray.Dataset

• Dimensions:
  • depth: 101
  • dualDepth: 101
  • time: 8741
• Coordinates: (3)
  • depth
    (depth)
    float64
    -0.995 -0.985 -0.975 ... -0.005 0.0

    units :

    m

    long_name :

    Soil depth.

    array([-0.995, -0.985, -0.975, -0.965, -0.955, -0.945, -0.935, -0.925, -0.915,
           -0.905, -0.895, -0.885, -0.875, -0.865, -0.855, -0.845, -0.835, -0.825,
           -0.815, -0.805, -0.795, -0.785, -0.775, -0.765, -0.755, -0.745, -0.735,
           -0.725, -0.715, -0.705, -0.695, -0.685, -0.675, -0.665, -0.655, -0.645,
           -0.635, -0.625, -0.615, -0.605, -0.595, -0.585, -0.575, -0.565, -0.555,
           -0.545, -0.535, -0.525, -0.515, -0.505, -0.495, -0.485, -0.475, -0.465,
           -0.455, -0.445, -0.435, -0.425, -0.415, -0.405, -0.395, -0.385, -0.375,
           -0.365, -0.355, -0.345, -0.335, -0.325, -0.315, -0.305, -0.295, -0.285,
           -0.275, -0.265, -0.255, -0.245, -0.235, -0.225, -0.215, -0.205, -0.195,
           -0.185, -0.175, -0.165, -0.155, -0.145, -0.135, -0.125, -0.115, -0.105,
           -0.095, -0.085, -0.075, -0.065, -0.055, -0.045, -0.035, -0.025, -0.015,
           -0.005,  0.   ])

  • dualDepth
    (dualDepth)
    float64
    -1.0 -0.99 -0.98 ... -0.01 0.0

    units :

    m

    long_name :

    Dual soil depth.

    array([-1.  , -0.99, -0.98, -0.97, -0.96, -0.95, -0.94, -0.93, -0.92, -0.91,
           -0.9 , -0.89, -0.88, -0.87, -0.86, -0.85, -0.84, -0.83, -0.82, -0.81,
           -0.8 , -0.79, -0.78, -0.77, -0.76, -0.75, -0.74, -0.73, -0.72, -0.71,
           -0.7 , -0.69, -0.68, -0.67, -0.66, -0.65, -0.64, -0.63, -0.62, -0.61,
           -0.6 , -0.59, -0.58, -0.57, -0.56, -0.55, -0.54, -0.53, -0.52, -0.51,
           -0.5 , -0.49, -0.48, -0.47, -0.46, -0.45, -0.44, -0.43, -0.42, -0.41,
           -0.4 , -0.39, -0.38, -0.37, -0.36, -0.35, -0.34, -0.33, -0.32, -0.31,
           -0.3 , -0.29, -0.28, -0.27, -0.26, -0.25, -0.24, -0.23, -0.22, -0.21,
           -0.2 , -0.19, -0.18, -0.17, -0.16, -0.15, -0.14, -0.13, -0.12, -0.11,
           -0.1 , -0.09, -0.08, -0.07, -0.06, -0.05, -0.04, -0.03, -0.02, -0.01,
            0.  ])

  • time
    (time)
    datetime64[ns]
    2023-04-14T19:00:00 ... 2024-04-...

    long_name :

    Time.

    array(['2023-04-14T19:00:00.000000000', '2023-04-14T20:00:00.000000000',
           '2023-04-14T21:00:00.000000000', ..., '2024-04-12T21:00:00.000000000',
           '2024-04-12T22:00:00.000000000', '2024-04-12T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (20)
  • psi
    (time, depth)
    float64
    ...

    units :

    m

    long_name :

    Water suction.

    [882841 values with dtype=float64]

  • psiIC
    (depth)
    float64
    ...

    units :

    m

    long_name :

    Initial condition for water suction.

    array([-0.005, -0.015, -0.025, -0.035, -0.045, -0.055, -0.065, -0.075, -0.085,
           -0.095, -0.105, -0.115, -0.125, -0.135, -0.145, -0.155, -0.165, -0.175,
           -0.185, -0.195, -0.205, -0.215, -0.225, -0.235, -0.245, -0.255, -0.265,
           -0.275, -0.285, -0.295, -0.305, -0.315, -0.325, -0.335, -0.345, -0.355,
           -0.365, -0.375, -0.385, -0.395, -0.405, -0.415, -0.425, -0.435, -0.445,
           -0.455, -0.465, -0.475, -0.485, -0.495, -0.505, -0.515, -0.525, -0.535,
           -0.545, -0.555, -0.565, -0.575, -0.585, -0.595, -0.605, -0.615, -0.625,
           -0.635, -0.645, -0.655, -0.665, -0.675, -0.685, -0.695, -0.705, -0.715,
           -0.725, -0.735, -0.745, -0.755, -0.765, -0.775, -0.785, -0.795, -0.805,
           -0.815, -0.825, -0.835, -0.845, -0.855, -0.865, -0.875, -0.885, -0.895,
           -0.905, -0.915, -0.925, -0.935, -0.945, -0.955, -0.965, -0.975, -0.985,
           -0.995, -1.   ])

  • rootDensityIC
    (depth)
    float64
    ...

    units :

    m

    long_name :

    Initial condition for root depth.

    array([0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
           0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
           0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
           0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,
           5.033333e-05, 5.100000e-05, 5.166667e-05, 5.233333e-05, 5.300000e-05,
           5.366667e-05, 5.433333e-05, 5.500000e-05, 5.566667e-05, 5.633333e-05,
           5.700000e-05, 5.766667e-05, 5.833333e-05, 5.900000e-05, 5.966667e-05,
           6.033333e-05, 6.100000e-05, 6.166667e-05, 6.233333e-05, 6.300000e-05,
           6.366667e-05, 6.433333e-05, 6.500000e-05, 6.566667e-05, 6.633333e-05,
           6.700000e-05, 6.766667e-05, 6.833333e-05, 6.900000e-05, 6.966667e-05,
           7.150000e-05, 7.450000e-05, 7.750000e-05, 8.050000e-05, 8.350000e-05,
           8.650000e-05, 8.950000e-05, 9.250000e-05, 9.550000e-05, 9.850000e-05,
           1.010000e-04, 1.030000e-04, 1.050000e-04, 1.070000e-04, 1.090000e-04,
           1.110000e-04, 1.130000e-04, 1.150000e-04, 1.170000e-04, 1.190000e-04,
           1.180000e-04, 1.140000e-04, 1.100000e-04, 1.060000e-04, 1.020000e-04,
           9.800000e-05, 9.400000e-05, 9.000000e-05, 8.600000e-05, 8.200000e-05,
           7.850000e-05, 7.550000e-05, 7.250000e-05, 6.950000e-05, 6.650000e-05,
           6.350000e-05, 6.050000e-05, 5.750000e-05, 5.450000e-05, 5.150000e-05,
           4.850000e-05, 4.550000e-05, 4.250000e-05, 3.950000e-05, 3.650000e-05,
           3.350000e-05, 3.050000e-05, 2.750000e-05, 2.450000e-05, 2.150000e-05,
           0.000000e+00])

  • Temperature
    (depth)
    float64
    ...

    units :

    K

    long_name :

    Temperature.

    array([273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15, 273.15,
           273.15, 293.15])

  • theta
    (time, depth)
    float64
    ...

    units :

    long_name :

    theta for within soil and water depth.

    [882841 values with dtype=float64]

  • waterVolume
    (time, depth)
    float64
    ...

    units :

    m

    long_name :

    Water volume in each control volume

    [882841 values with dtype=float64]

  • darcyVelocity
    (time, dualDepth)
    float64
    ...

    units :

    m/s

    long_name :

    Darcy velocity.

    [882841 values with dtype=float64]

  • ets
    (time, depth)
    float64
    ...

    units :

    m

    long_name :

    Transpired stressed water in Richards

    [882841 values with dtype=float64]

  • StressWaters
    (time, depth)
    float64
    ...

    units :

    -

    long_name :

    Water stress in each control volume

    [882841 values with dtype=float64]

  • StressWater
    (time)
    float64
    ...

    units :

    -

    long_name :

    water stress

    array([0.800386, 0.799274, 0.798177, ..., 0.62831 , 0.628138, 0.627965])

  • EvaporationStressWater
    (time)
    float64
    ...

    units :

    m

    long_name :

    evaporation Stress Water

    array([0.766731, 0.76623 , 0.765767, ..., 0.518043, 0.518638, 0.519205])

  • StressSun
    (time)
    float64
    ...

    units :

    -

    long_name :

    total stressSun

    array([0.1073  , 0.090727, 0.085049, ..., 0.067564, 0.067357, 0.066977])

  • StressShade
    (time)
    float64
    ...

    units :

    m

    long_name :

    total stressShade

    array([0.1073  , 0.090727, 0.085049, ..., 0.067564, 0.067357, 0.066977])

  • StressedETs
    (time, depth)
    float64
    ...

    units :

    mm

    long_name :

    EvapoTranspired stressed water from BrokerGEO

    [882841 values with dtype=float64]

  • StressedTs
    (time, depth)
    float64
    ...

    units :

    mm

    long_name :

    Transpired stressed water from BrokerGEO

    [882841 values with dtype=float64]

  • StressedEs
    (time, depth)
    float64
    ...

    units :

    mm

    long_name :

    Evaporated stressed water from BrokerGEO

    [882841 values with dtype=float64]

  • RootDensity
    (time, depth)
    float64
    ...

    units :

    -

    long_name :

    Root density in each control volume

    [882841 values with dtype=float64]

  • errorWaterVolume
    (time)
    float64
    ...

    units :

    m

    long_name :

    Volume error at each time step.

    array([ 1.277339e-12, -1.445377e-17,  8.520474e-17, ..., -1.159351e-16,
            5.234621e-17,  1.326496e-17])

  • runOff
    (time)
    float64
    ...

    units :

    m/s

    long_name :

    run off

    array([0., 0., 0., ..., 0., 0., 0.])

  • controlVolume
    (depth)
    float64
    ...

    units :

    m

    long_name :

    dimension of each control volumes

    array([0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
           0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
           0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
           0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
           0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
           0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
           0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
           0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
           0.01, 0.01, 0.01, 0.01, 1.  ])

• Attributes: (10)

  Description_of_the_problem :

  GEOSPACE simulation

  Top_boundary_condition :

  Top Coupled

  Bottom_boundary_condition :

  Bottom Free Drainage

  path_top_boundary_condition :

  C:\Users\marco\OMS_Project\OMS_Project_GEOSPACE-1D-main//data/Montecurto/1/precip_1.csv

  path_bottom_boundary_condition :

  C:\Users\marco\OMS_Project\OMS_Project_GEOSPACE-1D-main//data/Montecurto/1/Montecurto_nan.csv

  path_grid :

  C:\Users\marco\OMS_Project\OMS_Project_GEOSPACE-1D-main//data/Grid_NetCDF/Grid_GEOSPACE_montecurto07.nc

  time_delta :

  swrc_model :

  {Water Depth,Van Genuchten}

  soil_hydraulic_conductivity_model :

  {null,Mualem Van Genuchten}

  interface_conductivity_model :

  max

prec = io_csv.pandas_read_OMS_timeseries(project_path + '/data/Montecurto/1/precip_1.csv',datetime_index=False)
#prec = io_csv.pandas_read_OMS_timeseries(project_path + '/data/Montecurto/1/precip_dry_1.csv',datetime_index=False)
prec.head()

	Datetime	1
0	2023-04-14 19:00:00	0.0
1	2023-04-14 20:00:00	0.0
2	2023-04-14 21:00:00	0.0
3	2023-04-14 22:00:00	0.0
4	2023-04-14 23:00:00	0.0

# Impostazioni iniziali
fig, ax = plt.subplots(2, 1, sharex='col', figsize=(1400/100, 800/100)) 
norm = matplotlib.colors.Normalize(vmin=0.05, vmax=0.4)
#norm = matplotlib.colors.Normalize(vmin=np.amin(ds.psi.values), vmax=np.amax(ds.psi.values))

# Grafico superiore
ax[0].plot(prec['Datetime'], prec['1'])
ax[0].set_ylabel('Precipitation-Irrigation [mm/h]', fontname='Arial',size=14)
ax[0].tick_params(axis='both', which='major', labelsize=14)

# Modifica dei colori della griglia del primo plot
ax[0].grid(color='lightgray', linestyle='-', linewidth=0.8)  

# Aggiunta del contorno esterno scuro al primo grafico
ax[0].spines['top'].set_color('black')
ax[0].spines['bottom'].set_color('black')
ax[0].spines['left'].set_color('black')
ax[0].spines['right'].set_color('black')

# Grafico inferiore con la color bar
a = ds.theta.plot(ax=ax[1], y='depth', add_colorbar=False, cmap=my_cmap, norm=norm)
ax[1].set_ylabel('Depth [m]', fontname='Arial',size=14)
ax[1].set_xlabel('')
ax[1].xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
ax[1].xaxis.set_tick_params(rotation=0)
ax[1].tick_params(axis='both', which='major', labelsize=14) 

# Aggiunta della color bar
cbar_ax = fig.add_axes([0.91, 0.12, 0.02, 0.36])
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=norm)
cbar = fig.colorbar(sm, cax=cbar_ax)
cbar.set_label('$\\theta$ [-]', rotation=0, labelpad=22, fontname='Arial',size=14)

# Settaggio dei valori della color bar
#cbar.set_ticks([val for val in np.linspace(np.amin(ds.psi.values), np.amax(ds.psi.values), num=5)])
cbar.set_ticks([val for val in np.linspace(0.05,0.4, num=5)])
cbar.ax.tick_params(labelsize=14) 

# Salva l'immagine
#plt.savefig('ProsperoMontecuro_totalstress_06_rad-theta.png', dpi=300, bbox_inches='tight')  # Imposta il nome desiderato per l'immagine e regola dpi e bbox se necessario


plt.show()

# Impostazioni iniziali
fig, ax = plt.subplots(2, 1, sharex='col', figsize=(1400/100, 800/100)) 
#norm = matplotlib.colors.Normalize(vmin=0.05, vmax=0.4)
norm = matplotlib.colors.Normalize(vmin=np.amin(ds.StressWaters.values), vmax=np.amax(ds.StressWaters.values))

# Grafico superiore
ax[0].plot(prec['Datetime'], prec['1'])
ax[0].set_ylabel('Precipitation-Irrigation [mm/h]', fontname='Arial',size=14)
ax[0].tick_params(axis='both', which='major', labelsize=14)

# Modifica dei colori della griglia del primo plot
ax[0].grid(color='lightgray', linestyle='-', linewidth=0.8)  

# Aggiunta del contorno esterno scuro al primo grafico
ax[0].spines['top'].set_color('black')
ax[0].spines['bottom'].set_color('black')
ax[0].spines['left'].set_color('black')
ax[0].spines['right'].set_color('black')

# Grafico inferiore con la color bar
a = ds.StressWaters.plot(ax=ax[1], y='depth', add_colorbar=False, cmap=my_cmap, norm=norm)
ax[1].set_ylabel('Depth [m]', fontname='Arial',size=14)
ax[1].set_xlabel('')
ax[1].xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
ax[1].xaxis.set_tick_params(rotation=0)
ax[1].tick_params(axis='both', which='major', labelsize=14) 

# Aggiunta della color bar
cbar_ax = fig.add_axes([0.91, 0.12, 0.02, 0.36])
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=norm)
cbar = fig.colorbar(sm, cax=cbar_ax)
cbar.set_label('StressWaters [-]', rotation=90, labelpad=22, fontname='Arial',size=14)

# Settaggio dei valori della color bar
cbar.set_ticks([val for val in np.linspace(np.amin(ds.StressWaters.values), np.amax(ds.StressWaters.values), num=5)])
#cbar.set_ticks([val for val in np.linspace(0.05,0.4, num=5)])
cbar.ax.tick_params(labelsize=14) 

# Salva l'immagine
#plt.savefig('ProsperoMontecuro_totalstress_net_06_StressWaters.png', dpi=300, bbox_inches='tight')  # Imposta il nome desiderato per l'immagine e regola dpi e bbox se necessario
#plt.savefig('ProsperoMontecuro_totalstress_07_StressWaters.png', dpi=300, bbox_inches='tight')  # Imposta il nome desiderato per l'immagine e regola dpi e bbox se necessario


plt.show()

root_depth = -0.8
evaporation_layer_depth = -0.2

#myDepth = [-0.10,-0.30,-0.50,-0.70,-1.00,-1.5]
myDepth = [-0.10,-0.30,-0.50,-0.70,-0.85,-1.00]
myDate = ['2023-04-14 19:00','2023-07-01 01:00','2023-07-03 01:00','2023-07-04 01:00','2023-07-05 01:00','2023-07-08 01:00','2023-07-25 01:00']
#myDate = ['2023-10-01 01:00','2023-10-21 01:00']#,'2023-10-21 01:00','2023-06-22 01:00','2023-06-23 01:00','2023-06-24 01:00','2023-06-25 01:00','2023-06-26 01:00']

colorblind= ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f'] # Creare subplot con dimensioni personalizzate fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=['a) Water pressure at different depths', 'b) Water content at different depths'], row_heights=[0.5, 0.5], vertical_spacing=0.19) # Imposta la dimensione del testo dei titoli delle subplot for i in range(1, 3): fig.layout.annotations[i-1].update(font=dict(size=22)) # Subplot a for i in range(len(myDepth)): fig.add_trace(go.Scatter(x=ds.time, y=ds.psi.sel(depth=myDepth[i], method='nearest'), name=str(myDepth[i])+' m',line=dict(width=2,color=colorblind[i])), row=1, col=1) # Subplot b for i in range(len(myDepth)): fig.add_trace(go.Scatter(x=ds.time, y=ds.theta.sel(depth=myDepth[i], method='nearest'),showlegend=False,line=dict(width=2,color=colorblind[i])), row=2, col=1) # Aggiornare il layout del grafico fig.update_layout( height=800, width=1300, font_family="Arial", font_color="Black", title_font_family="Arial", title_font_color="Black", font=dict(size=22), plot_bgcolor="white", legend=dict(orientation='h', y=1.15, x=0.25), ) # Subplot a fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=1, col=1, zeroline=True, zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d') fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=1, col=1, zeroline=True, zerolinecolor="darkgray", zerolinewidth=1, title={"text": 'ψ [m]', "font": {"size": 20, "family": "Arial"}}, tickangle=0,tickfont=dict(size=20)) # Subplot b fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=2, col=1, zeroline=True, zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d') fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=2, col=1, zeroline=True, zerolinecolor="darkgray", zerolinewidth=1, title={"text": 'θ [-]', "font": {"size": 20, "family": "Arial"}},tickangle=0,tickfont=dict(size=20)) fig.add_shape(type="rect",xref="paper",yref="paper",x0=fig['layout']['xaxis']['domain'][0],y0=fig['layout']['yaxis']['domain'][0],x1=fig['layout']['xaxis']['domain'][1],y1=fig['layout']['yaxis']['domain'][1],line=dict(color="darkgray", width=2)) fig.add_shape(type="rect",xref="paper",yref="paper",x0=fig['layout']['xaxis2']['domain'][0],y0=fig['layout']['yaxis2']['domain'][0],x1=fig['layout']['xaxis2']['domain'][1],y1=fig['layout']['yaxis2']['domain'][1],line=dict(color="darkgray", width=2)) fig.show()

colorblind= ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f']
fig = go.Figure()
# Creare subplot con dimensioni personalizzate
#fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
 #                   subplot_titles=['a) Water pressure at different depths', 'b) Water content at different depths'],
  #                  row_heights=[0.5, 0.5],
   #                 vertical_spacing=0.19)

# Imposta la dimensione del testo dei titoli delle subplot
#for i in range(1, 3):
 #   fig.layout.annotations[i-1].update(font=dict(size=22))

# Subplot a
#for i in range(len(myDepth)):
   # fig.add_trace(go.Scatter(x=ds.time, y=ds.psi.sel(depth=myDepth[i], method='nearest'), name=str(myDepth[i])+' m',line=dict(width=2,color=colorblind[i])), row=1, col=1)


# Subplot b
for i in range(len(myDepth)):
    fig.add_trace(go.Scatter(x=ds.time, y=ds.theta.sel(depth=myDepth[i], method='nearest'), name=str(myDepth[i])+' m',showlegend=True,line=dict(width=2,color=colorblind[i])))

# Aggiornare il layout del grafico
fig.update_layout(
    height=500, width=1300,
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    font=dict(size=22),
    plot_bgcolor="white",
    legend=dict(orientation='h', y=1.15, x=0.25),
)

# Subplot a
#fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=1, col=1, zeroline=True, 
 #                zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d')

#fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=1, col=1, zeroline=True, 
 #                zerolinecolor="darkgray", zerolinewidth=1, title={"text": 'ψ [m]', "font": {"size": 20, "family": "Arial"}}, tickangle=0,tickfont=dict(size=20))

# Subplot b
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, 
                 zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d')


fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, 
                 zerolinecolor="darkgray", zerolinewidth=1, title={"text": 'Water Content θ [-]', "font": {"size": 20, "family": "Arial"}},tickangle=0,tickfont=dict(size=20))

#fig.add_shape(type="rect",xref="paper",yref="paper",x0=fig['layout']['xaxis']['domain'][0],y0=fig['layout']['yaxis']['domain'][0],x1=fig['layout']['xaxis']['domain'][1],y1=fig['layout']['yaxis']['domain'][1],line=dict(color="darkgray", width=2))
#fig.add_shape(type="rect",xref="paper",yref="paper",x0=fig['layout']['xaxis2']['domain'][0],y0=fig['layout']['yaxis2']['domain'][0],x1=fig['layout']['xaxis2']['domain'][1],y1=fig['layout']['yaxis2']['domain'][1],line=dict(color="darkgray", width=2))
fig.add_shape(type="rect", xref="paper", yref="paper", x0=0, y0=0, x1=1, y1=1, line=dict(color="darkgray", width=2))

fig.show()

# Creare subplot con dimensioni personalizzate
fig = make_subplots(rows=1, cols=2, shared_yaxes=True,
                    subplot_titles=['a) Water pressure at different times', 'b) Water content at different times'],
                    column_widths=[0.5, 0.5],
                    horizontal_spacing=0.1)

# Imposta la dimensione del testo dei titoli delle subplot
for i in range(1, 3):
    fig.layout.annotations[i-1].update(font=dict(size=22))

# Subplot a

fig.add_trace(go.Scatter(x=ds.psi.sel(time=myDate[0]), y=ds.depth.values[:] ,name='Initial Condition',line=dict(width=2.5,color='black')), row=1, col=1)              
for i in range(1, len(myDate)):
    fig.add_trace(go.Scatter(x=ds.psi.sel(time=myDate[i]), y=ds.depth.values[:] ,name=str(myDate[i]),line=dict(width=2.5,color=colors[i-1])), row=1, col=1)
    

    
# Subplot b
fig.add_trace(go.Scatter(x=ds.theta.sel(time=myDate[0]), y=ds.depth.values[:] ,name=str(myDate[i]), showlegend=False, line=dict(width=2.5,color='black')), row=1, col=2)
for i in range(1, len(myDate)):
    fig.add_trace(go.Scatter(x=ds.theta.sel(time=myDate[i]), y=ds.depth.values[:] ,name=str(myDate[i]), showlegend=False, line=dict(width=2.5,color=colors[i-1])), row=1, col=2)
    
    
# Aggiornare il layout del grafico
fig.update_layout(
    height=800, width=1400,
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    font=dict(size=22),
    plot_bgcolor="white",
    #legend=dict(orientation='h', y=1.15, x=0.2),
)



## Subplot a
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=1, col=1, zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True, tickfont=dict(size=20),title={"text": 'ψ [m]', "font": {"size": 20, "family": "Arial"}})

fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=1, col=1, zeroline=True, zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True, tickfont=dict(size=20),title={"text": 'Soil depth [m]', "font": {"size": 20, "family": "Arial"}})

# Subplot b
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=1, col=2, zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True, tickfont=dict(size=20),title={"text": 'θ [-]', "font": {"size": 22, "family": "Arial"}})

fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', row=1, col=2, zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True, tickfont=dict(size=20))

fig.add_shape(type="rect",xref="paper",yref="paper",x0=fig['layout']['xaxis']['domain'][0],y0=fig['layout']['yaxis']['domain'][0],x1=fig['layout']['xaxis']['domain'][1],y1=fig['layout']['yaxis']['domain'][1],line=dict(color="darkgray", width=2))
fig.add_shape(type="rect",xref="paper",yref="paper",x0=fig['layout']['xaxis2']['domain'][0],y0=fig['layout']['yaxis2']['domain'][0],x1=fig['layout']['xaxis2']['domain'][1],y1=fig['layout']['yaxis2']['domain'][1],line=dict(color="darkgray", width=2))


fig.show()

# Creare subplot con dimensioni personalizzate
fig = go.Figure()

# Subplot a

fig.add_trace(go.Scatter(x=ds.theta.sel(time=myDate[0]), y=ds.depth.values[:100] ,name='Initial Condition',line=dict(width=2.5,color='black')))              
for i in range(1, len(myDate)):
    fig.add_trace(go.Scatter(x=ds.theta.sel(time=myDate[i]), y=ds.depth.values[:100] ,name=str(myDate[i]),line=dict(width=2.5,color=colors[i-1])))
    
fig.add_shape(go.layout.Shape(type='line', x0=0, x1=1, y0=root_depth, y1=root_depth, line=dict(color='black', width=2, dash ='dash')))
fig.add_shape(go.layout.Shape(type='line', x0=0, x1=1, y0=evaporation_layer_depth, y1=evaporation_layer_depth, line=dict(color='black', width=2, dash ='dashdot')))

# Adding Annotation on subplot a
fig.add_annotation(go.layout.Annotation(text="evaporation depth", x=0.01,y=0.81,showarrow=False,xref="paper",yref="paper",font=dict(size=18)))
fig.add_annotation(go.layout.Annotation(text="root depth", x=0.01,y=0.23,showarrow=False,xref="paper",yref="paper",font=dict(size=18)))


# Aggiornare il layout del grafico
fig.update_layout(
    height=800, width=800,
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    font=dict(size=22),
    plot_bgcolor="white",
    #legend=dict(orientation='h', y=1.15, x=0.2),
)




## Subplot 
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True,range=[0.1,0.45],zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True, tickfont=dict(size=20),title={"text": 'Water Content [-]', "font": {"size": 20, "family": "Arial"}})

fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True, tickfont=dict(size=20),title={"text": 'Soil depth [m]', "font": {"size": 20, "family": "Arial"}})


fig.add_shape(type="rect", xref="paper", yref="paper", x0=0, y0=0, x1=1, y1=1, line=dict(color="darkgray", width=2))

fig.show()

fig = px.line()
fig.add_trace(go.Scatter(y=ds.StressWater, x=ds.time.values[:], mode='lines',line=dict(color="black", width=2.5)))

fig.update_layout(
    height=400, width=1200,
    title='',
    #xaxis_title="Date"
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    yaxis_title="[-]",
    #legend_title="Date",
    font=dict(size=16),
    legend=dict(yanchor="top", y=0.99, xanchor="right", x=0.99, title=""),
    plot_bgcolor="white",
)
fig.add_shape(type="rect", xref="paper", yref="paper", x0=fig['layout']['xaxis']['domain'][0], y0=fig['layout']['yaxis']['domain'][0], x1=fig['layout']['xaxis']['domain'][1], y1=fig['layout']['yaxis']['domain'][1], line=dict(color="darkgray", width=2))
fig.update_yaxes(title={"text": 'Representative Water Stress [-]', "font": {"size": 20, "family": "Arial"}},showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1,tickfont=dict(size=20))
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d')
fig.show()

fig = px.line()
fig.add_trace(go.Scatter(y=ds.darcyVelocity.sel(dualDepth=ds.dualDepth[0], time=ds.time.values[:]).values*-3600000, x=ds.time.values[:], mode='lines',line=dict(color="black", width=2.5)))

fig.update_layout(
    height=400, width=1200,
    title='',
    #xaxis_title="Date"
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    yaxis_title="",
    #legend_title="Date",
    font=dict(size=16),
    legend=dict(yanchor="top", y=0.99, xanchor="right", x=0.99, title=""),
    plot_bgcolor="white",
)
fig.add_shape(type="rect", xref="paper", yref="paper", x0=fig['layout']['xaxis']['domain'][0], y0=fig['layout']['yaxis']['domain'][0], x1=fig['layout']['xaxis']['domain'][1], y1=fig['layout']['yaxis']['domain'][1], line=dict(color="darkgray", width=2))
fig.update_yaxes(title={"text": 'Bottom Flux [mm/h]', "font": {"size": 20, "family": "Arial"}},showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1,tickfont=dict(size=20))
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d')
fig.show()

Plot Evapotranspiration

os.chdir(project_path+'/output/Montecurto')

dfE= pd.read_csv('Evaporation_1_'+lab+'.csv',skiprows=6,parse_dates=[1])
dfE = dfE.drop(['Format'],axis=1) 
dfE.columns = ['Datetime','E_sim']

dfT = pd.read_csv('Transpiration_1_'+lab+'.csv',skiprows=6,parse_dates=[1])
dfT = dfT.drop(['Format'],axis=1) 
dfT.columns = ['Datetime','T_sim']

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(
    go.Scatter(x=dfT.Datetime, y=dfT.T_sim, name="Evaporation",line = dict(color = '#117733', width=2)),
    secondary_y=False,
)

fig.add_trace(
    go.Scatter(x=dfE.Datetime, y=dfE.E_sim, name="Transpiration",line = dict(color = '#EE7733', width=2)),
    secondary_y=True,
)


fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray',zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d')

fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1)
fig.update_yaxes(title={"text": 'Transpiration [mm/h]', "font": {"size": 20, "family": "Arial"}}, secondary_y=False,title_standoff=20,tickfont=dict(size=20))
fig.update_yaxes(title={"text": 'Evaporation [mm/h]', "font": {"size": 20, "family": "Arial"}}, secondary_y=True,title_standoff=20,tickfont=dict(size=20))

fig.add_shape(type="rect", xref="paper", yref="paper", x0=fig['layout']['xaxis']['domain'][0], y0=fig['layout']['yaxis']['domain'][0], x1=fig['layout']['xaxis']['domain'][1], y1=fig['layout']['yaxis']['domain'][1], line=dict(color="darkgray", width=2))




fig.update_layout(
    height=500, width=1400,
    #title='OliveTree data',
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    plot_bgcolor="white",
    font=dict(size=22),
    legend=dict(orientation='h', y=1.15, x=0.35)
)

fig.show()

fig = px.line()
fig.add_trace(go.Scatter(x=dfT['Datetime'], y=dfT['T_sim'], mode='lines',line=dict(color="green", width=1.5)))


fig.update_layout(
    height=500, width=1400,
    title='',
    #xaxis_title="Date"
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    #legend_title="Date",
    font=dict(size=20),
    legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01, title=""),
    plot_bgcolor="white",
)
fig.add_shape(type="rect", xref="paper", yref="paper", x0=fig['layout']['xaxis']['domain'][0], y0=fig['layout']['yaxis']['domain'][0], x1=fig['layout']['xaxis']['domain'][1], y1=fig['layout']['yaxis']['domain'][1], line=dict(color="darkgray", width=2))
fig.update_yaxes(title={"text": 'Transpiration [mm/h]', "font": {"size": 20, "family": "Arial"}},showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1,tickfont=dict(size=20))
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d')

fig.show()

dfET = pd.read_csv('EvapoTranspiration_1_'+lab+'.csv',skiprows=6,parse_dates=[1])
dfET = dfET.drop(['Format'],axis=1) 
dfET.columns = ['Datetime','ET_sim']

fig = px.line()
fig.add_trace(go.Scatter(x=dfET['Datetime'], y=dfET['ET_sim'], mode='lines',line=dict(color="green", width=1.5)))


fig.update_layout(
    height=500, width=1400,
    title='',
    #xaxis_title="Date"
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    #legend_title="Date",
    font=dict(size=20),
    legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01, title=""),
    plot_bgcolor="white",
)
fig.add_shape(type="rect", xref="paper", yref="paper", x0=fig['layout']['xaxis']['domain'][0], y0=fig['layout']['yaxis']['domain'][0], x1=fig['layout']['xaxis']['domain'][1], y1=fig['layout']['yaxis']['domain'][1], line=dict(color="darkgray", width=2))
fig.update_yaxes(title={"text": 'Evapotranspiration [mm/h]', "font": {"size": 20, "family": "Arial"}},showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1,tickfont=dict(size=20))
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d')

fig.show()

#fig.write_html('ProsperoMontecuro_totalstress_07_EvapoTranspiration.html')

dfET.head()

	Datetime	ET_sim
0	2023-04-14 19:00:00	0.040590
1	2023-04-14 20:00:00	0.035707
2	2023-04-14 21:00:00	0.013602
3	2023-04-14 22:00:00	0.007495
4	2023-04-14 23:00:00	0.010473

dfET.set_index('Datetime',inplace=True)
dfET.head()

	ET_sim
Datetime
2023-04-14 19:00:00	0.040590
2023-04-14 20:00:00	0.035707
2023-04-14 21:00:00	0.013602
2023-04-14 22:00:00	0.007495
2023-04-14 23:00:00	0.010473

dfETg=dfET.resample('D').sum()
dfETg.head()

	ET_sim
Datetime
2023-04-14	0.107867
2023-04-15	3.333873
2023-04-16	3.077985
2023-04-17	2.303740
2023-04-18	3.889938

dfETg.reset_index(inplace=True)
dfETg.head()

	Datetime	ET_sim
0	2023-04-14	0.107867
1	2023-04-15	3.333873
2	2023-04-16	3.077985
3	2023-04-17	2.303740
4	2023-04-18	3.889938

fig = px.line()
fig.add_trace(go.Scatter(x=dfETg['Datetime'], y=dfETg['ET_sim'], mode='lines',line=dict(color="green", width=1.5)))


fig.update_layout(
    height=500, width=1400,
    title='',
    #xaxis_title="Date"
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    #legend_title="Date",
    font=dict(size=20),
    legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01, title=""),
    plot_bgcolor="white",
)
fig.add_shape(type="rect", xref="paper", yref="paper", x0=fig['layout']['xaxis']['domain'][0], y0=fig['layout']['yaxis']['domain'][0], x1=fig['layout']['xaxis']['domain'][1], y1=fig['layout']['yaxis']['domain'][1], line=dict(color="darkgray", width=2))
fig.update_yaxes(title={"text": 'Evapotranspiration [mm/d]', "font": {"size": 20, "family": "Arial"}},showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1,tickfont=dict(size=20))
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d')
fig.show()

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces

fig.add_trace(
    go.Scatter(x=dfET.Datetime, y=dfET.ET_sim, name="Hourly ET",line = dict(color = '#EE7733', width=2)),
    secondary_y=False,
)
fig.add_trace(
    go.Scatter(x=dfETg.Datetime, y=dfETg.ET_sim, name="Daily ET",line = dict(color = '#117733', width=3)),
    secondary_y=True,
)



fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray',zeroline=True,zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True,tickfont=dict(size=20), tickformat='%b %d')

fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1)
fig.update_yaxes(title={"text": 'EvapoTranspiration [mm/h]', "font": {"size": 20, "family": "Arial"}}, secondary_y=False,title_standoff=20,tickfont=dict(size=20))
fig.update_yaxes(title={"text": 'EvapoTranspiration [mm/d]', "font": {"size": 20, "family": "Arial"}}, secondary_y=True,title_standoff=20,tickfont=dict(size=20))

fig.add_shape(type="rect", xref="paper", yref="paper", x0=fig['layout']['xaxis']['domain'][0], y0=fig['layout']['yaxis']['domain'][0], x1=fig['layout']['xaxis']['domain'][1], y1=fig['layout']['yaxis']['domain'][1], line=dict(color="darkgray", width=2))




fig.update_layout(
    height=500, width=1400,
    #title='OliveTree data',
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    plot_bgcolor="white",
    font=dict(size=22),
    legend=dict(orientation='h', y=1.15, x=0.35)
)

fig.show()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_22536\3538150904.py in <module>
      5 
      6 fig.add_trace(
----> 7     go.Scatter(x=dfET.Datetime, y=dfET.ET_sim, name="Hourly ET",line = dict(color = '#EE7733', width=2)),
      8     secondary_y=False,
      9 )

~\anaconda3\envs\geoframe_parigi\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
   5485         ):
   5486             return self[name]
-> 5487         return object.__getattribute__(self, name)
   5488 
   5489     def __setattr__(self, name: str, value) -> None:

AttributeError: 'DataFrame' object has no attribute 'Datetime'

# Creare il grafico
fig = px.line()
fig.add_trace(go.Scatter(y=ds.errorWaterVolume, x=ds.time.values[:], mode='lines',line=dict(color="black", width=1.5)))

# Aggiornare il layout del grafico
fig.update_layout(
    height=400, width=1200,
    title='',
    font_family="Arial",
    font_color="Black",
    title_font_family="Arial",
    title_font_color="Black",
    yaxis_title="",
    font=dict(size=18),
    legend=dict(yanchor="top", y=0.99, xanchor="right", x=0.99, title=""),
    plot_bgcolor="white",
)

# Aggiungere una forma
fig.add_shape(type="rect", xref="paper", yref="paper", x0=fig['layout']['xaxis']['domain'][0], y0=fig['layout']['yaxis']['domain'][0], x1=fig['layout']['xaxis']['domain'][1], y1=fig['layout']['yaxis']['domain'][1], line=dict(color="darkgray", width=2))

# Aggiornare l'aspetto dell'asse y
fig.update_yaxes(title={"text": 'Volume error at each time step [m]', "font": {"size": 20, "family": "Arial"}},showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1, tickfont=dict(size=20), showexponent='last', exponentformat='e')

# Aggiornare l'aspetto dell'asse x
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='lightgray', zeroline=True, zerolinecolor="darkgray", zerolinewidth=1, showticklabels=True, tickfont=dict(size=20), tickformat='%b %d')

# Mostrare il grafico
fig.show()